Development of a Maximum Specific Photosynthetic Rate Algorithm Based on Remote Sensing Data: a Case Study for the Atlantic Ocean
Bibliographic record
Abstract
New regional empirical algorithms were developed to obtain maximum specific photosynthetic rates of phytoplankton ( $$P_{m}^{B}$$ ) in the surface layer of the Atlantic Ocean. These algorithms were based on the dependence of $$P_{m}^{B}$$ on seawater temperature. Sea Surface Temperature remote sensing data and the PANGAEA global database of photosynthesis–irradiance parameters were used to test the algorithm. In addition, the variability in $$P_{m}^{B}$$ , both spatially (from 60° S to 85° N) and seasonally, (2002–2013) was estimated. The highest $$P_{m}^{B}$$ was obtained in December in areas of deep convection and the interaction between the Labrador Current and the Gulf Stream, while minimum values were observed in the northern and equatorial–tropical parts of the ocean during the time intervals between the phytoplankton blooms (March to September–October). In addition, existing $$P_{m}^{B}$$ and $$P_{{{\text{opt}}}}^{B}$$ algorithms used in primary production models, as well as the $$P_{m}^{B}$$ algorithm developed using temperature and chlorophyll a data from AMT-29, which were then tested using the PANGAEA dataset. The results show that the new $$P_{m}^{B}$$ algorithm developed using seawater temperature data with regionally adjusted empirical coefficients correlated best with the in situ data.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".